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more code blocks for readthedocs

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Summary: More rst syntax fixes

Reviewed By: davidsonic

Differential Revision: D40977328

fbshipit-source-id: a3a3accbf2ba7cd9c84a0a82d0265010764a9d61
This commit is contained in:
Jeremy Reizenstein 2022-11-29 03:13:40 -08:00 committed by Facebook GitHub Bot
parent 94f321fa3d
commit a2c6af9250
14 changed files with 167 additions and 174 deletions
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17
pytorch3d/io/pluggable.py
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@ -25,19 +25,16 @@ This module has the master functions for loading and saving data.
The main usage is via the IO object, and its methods
`load_mesh`, `save_mesh`, `load_pointcloud` and `save_pointcloud`.
For example, to load a mesh you might do
```
from pytorch3d.io import IO
For example, to load a mesh you might do::
mesh = IO().load_mesh("mymesh.obj")
```
from pytorch3d.io import IO
and to save a point cloud you might do
mesh = IO().load_mesh("mymesh.obj")
```
pcl = Pointclouds(...)
IO().save_pointcloud(pcl, "output_pointcloud.obj")
```
and to save a point cloud you might do::
pcl = Pointclouds(...)
IO().save_pointcloud(pcl, "output_pointcloud.obj")
"""

52
pytorch3d/io/ply_io.py
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@ -1067,33 +1067,33 @@ def load_ply(
is to use the IO.load_mesh and IO.load_pointcloud functions,
which can read more of the data.
Example .ply file format:
Example .ply file format::
ply
format ascii 1.0 { ascii/binary, format version number }
comment made by Greg Turk { comments keyword specified, like all lines }
comment this file is a cube
element vertex 8 { define "vertex" element, 8 of them in file }
property float x { vertex contains float "x" coordinate }
property float y { y coordinate is also a vertex property }
property float z { z coordinate, too }
element face 6 { there are 6 "face" elements in the file }
property list uchar int vertex_index { "vertex_indices" is a list of ints }
end_header { delimits the end of the header }
0 0 0 { start of vertex list }
0 0 1
0 1 1
0 1 0
1 0 0
1 0 1
1 1 1
1 1 0
4 0 1 2 3 { start of face list }
4 7 6 5 4
4 0 4 5 1
4 1 5 6 2
4 2 6 7 3
4 3 7 4 0
ply
format ascii 1.0 { ascii/binary, format version number }
comment made by Greg Turk { comments keyword specified, like all lines }
comment this file is a cube
element vertex 8 { define "vertex" element, 8 of them in file }
property float x { vertex contains float "x" coordinate }
property float y { y coordinate is also a vertex property }
property float z { z coordinate, too }
element face 6 { there are 6 "face" elements in the file }
property list uchar int vertex_index { "vertex_indices" is a list of ints }
end_header { delimits the end of the header }
0 0 0 { start of vertex list }
0 0 1
0 1 1
0 1 0
1 0 0
1 0 1
1 1 1
1 1 0
4 0 1 2 3 { start of face list }
4 7 6 5 4
4 0 4 5 1
4 1 5 6 2
4 2 6 7 3
4 3 7 4 0
Args:
f: A binary or text file-like object (with methods read, readline,

5
pytorch3d/ops/points_to_volumes.py
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@ -208,8 +208,8 @@ def add_pointclouds_to_volumes(
of `initial_volumes` with its `features` and `densities` updated with the
result of the pointcloud addition.
Example:
```
Example::
# init a random point cloud
pointclouds = Pointclouds(
points=torch.randn(4, 100, 3), features=torch.rand(4, 100, 5)
@ -229,7 +229,6 @@ def add_pointclouds_to_volumes(
initial_volumes=initial_volumes,
mode="trilinear",
)
```
Args:
pointclouds: Batch of 3D pointclouds represented with a `Pointclouds`

6
pytorch3d/renderer/implicit/harmonic_embedding.py
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@ -21,8 +21,8 @@ class HarmonicEmbedding(torch.nn.Module):
(i.e. vector along the last dimension) in `x`
into a series of harmonic features `embedding`,
where for each i in range(dim) the following are present
in embedding[...]:
```
in embedding[...]::
[
sin(f_1*x[..., i]),
sin(f_2*x[..., i]),
@ -34,7 +34,7 @@ class HarmonicEmbedding(torch.nn.Module):
cos(f_N * x[..., i]),
x[..., i], # only present if append_input is True.
]
```
where N corresponds to `n_harmonic_functions-1`, and f_i is a scalar
denoting the i-th frequency of the harmonic embedding.

12
pytorch3d/renderer/implicit/raymarching.py
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@ -25,20 +25,20 @@ class EmissionAbsorptionRaymarcher(torch.nn.Module):
(i.e. its density -> 1.0).
EA first utilizes `rays_densities` to compute the absorption function
along each ray as follows:
```
along each ray as follows::
absorption = cumprod(1 - rays_densities, dim=-1)
```
The value of absorption at position `absorption[..., k]` specifies
how much light has reached `k`-th point along a ray since starting
its trajectory at `k=0`-th point.
Each ray is then rendered into a tensor `features` of shape `(..., feature_dim)`
by taking a weighed combination of per-ray features `rays_features` as follows:
```
by taking a weighed combination of per-ray features `rays_features` as follows::
weights = absorption * rays_densities
features = (rays_features * weights).sum(dim=-2)
```
Where `weights` denote a function that has a strong peak around the location
of the first surface point that a given ray passes through.

33
pytorch3d/renderer/implicit/raysampling.py
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@ -32,23 +32,22 @@ class MultinomialRaysampler(torch.nn.Module):
have uniformly-spaced z-coordinates between a predefined
minimum and maximum depth.
The raysampler first generates a 3D coordinate grid of the following form:
```
/ min_x, min_y, max_depth -------------- / max_x, min_y, max_depth
/ /|
/ / | ^
/ min_depth min_depth / | |
min_x ----------------------------- max_x | | image
min_y min_y | | height
| | | |
| | | v
| | |
| | / max_x, max_y, ^
| | / max_depth /
min_x max_y / / n_pts_per_ray
max_y ----------------------------- max_x/ min_depth v
< --- image_width --- >
```
The raysampler first generates a 3D coordinate grid of the following form::
/ min_x, min_y, max_depth -------------- / max_x, min_y, max_depth
/ /|
/ / | ^
/ min_depth min_depth / | |
min_x ----------------------------- max_x | | image
min_y min_y | | height
| | | |
| | | v
| | |
| | / max_x, max_y, ^
| | / max_depth /
min_x max_y / / n_pts_per_ray
max_y ----------------------------- max_x/ min_depth v
< --- image_width --- >
In order to generate ray points, `MultinomialRaysampler` takes each 3D point of
the grid (with coordinates `[x, y, depth]`) and unprojects it

58
pytorch3d/renderer/implicit/renderer.py
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@ -41,13 +41,13 @@ class ImplicitRenderer(torch.nn.Module):
as well as the `volumetric_function` `Callable`, which defines a field of opacity
and feature vectors over the 3D domain of the scene.
A standard `volumetric_function` has the following signature:
```
def volumetric_function(
ray_bundle: Union[RayBundle, HeterogeneousRayBundle],
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]
```
A standard `volumetric_function` has the following signature::
def volumetric_function(
ray_bundle: Union[RayBundle, HeterogeneousRayBundle],
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]
With the following arguments:
`ray_bundle`: A RayBundle or HeterogeneousRayBundle object
containing the following variables:
@ -79,32 +79,32 @@ class ImplicitRenderer(torch.nn.Module):
Example:
A simple volumetric function of a 0-centered
RGB sphere with a unit diameter is defined as follows:
```
def volumetric_function(
ray_bundle: Union[RayBundle, HeterogeneousRayBundle],
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
RGB sphere with a unit diameter is defined as follows::
# first convert the ray origins, directions and lengths
# to 3D ray point locations in world coords
rays_points_world = ray_bundle_to_ray_points(ray_bundle)
def volumetric_function(
ray_bundle: Union[RayBundle, HeterogeneousRayBundle],
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
# set the densities as an inverse sigmoid of the
# ray point distance from the sphere centroid
rays_densities = torch.sigmoid(
-100.0 * rays_points_world.norm(dim=-1, keepdim=True)
)
# first convert the ray origins, directions and lengths
# to 3D ray point locations in world coords
rays_points_world = ray_bundle_to_ray_points(ray_bundle)
# set the ray features to RGB colors proportional
# to the 3D location of the projection of ray points
# on the sphere surface
rays_features = torch.nn.functional.normalize(
rays_points_world, dim=-1
) * 0.5 + 0.5
# set the densities as an inverse sigmoid of the
# ray point distance from the sphere centroid
rays_densities = torch.sigmoid(
-100.0 * rays_points_world.norm(dim=-1, keepdim=True)
)
# set the ray features to RGB colors proportional
# to the 3D location of the projection of ray points
# on the sphere surface
rays_features = torch.nn.functional.normalize(
rays_points_world, dim=-1
) * 0.5 + 0.5
return rays_densities, rays_features
return rays_densities, rays_features
```
"""
def __init__(self, raysampler: Callable, raymarcher: Callable) -> None:

12
pytorch3d/renderer/implicit/utils.py
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@ -73,13 +73,13 @@ def ray_bundle_to_ray_points(
extending each ray according to the corresponding length.
E.g. for 2 dimensional tensors `ray_bundle.origins`, `ray_bundle.directions`
and `ray_bundle.lengths`, the ray point at position `[i, j]` is:
```
and `ray_bundle.lengths`, the ray point at position `[i, j]` is::
ray_bundle.points[i, j, :] = (
ray_bundle.origins[i, :]
+ ray_bundle.directions[i, :] * ray_bundle.lengths[i, j]
)
```
Note that both the directions and magnitudes of the vectors in
`ray_bundle.directions` matter.
@ -109,13 +109,13 @@ def ray_bundle_variables_to_ray_points(
ray length:
E.g. for 2 dimensional input tensors `rays_origins`, `rays_directions`
and `rays_lengths`, the ray point at position `[i, j]` is:
```
and `rays_lengths`, the ray point at position `[i, j]` is::
rays_points[i, j, :] = (
rays_origins[i, :]
+ rays_directions[i, :] * rays_lengths[i, j]
)
```
Note that both the directions and magnitudes of the vectors in
`rays_directions` matter.

32
pytorch3d/renderer/opengl/opengl_utils.py
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@ -285,26 +285,26 @@ class _DeviceContextStore:
The EGL/CUDA contexts are not meant to be created and destroyed all the time,
and having multiple on a single device can be troublesome. Intended use is entirely
transparent to the user:
transparent to the user::
```
rasterizer1 = MeshRasterizerOpenGL(...some args...)
mesh1 = load_mesh_on_cuda_0()
rasterizer1 = MeshRasterizerOpenGL(...some args...)
mesh1 = load_mesh_on_cuda_0()
# Now rasterizer1 will request EGL/CUDA contexts from global_device_context_store
# on cuda:0, and since there aren't any, the store will create new ones.
rasterizer1.rasterize(mesh1)
# Now rasterizer1 will request EGL/CUDA contexts from
# global_device_context_store on cuda:0, and since there aren't any, the
# store will create new ones.
rasterizer1.rasterize(mesh1)
# rasterizer2 also needs EGL & CUDA contexts. But global_context_store already has
# them for cuda:0. Instead of creating new contexts, the store will tell rasterizer2
# to use them.
rasterizer2 = MeshRasterizerOpenGL(dcs)
rasterize2.rasterize(mesh1)
# rasterizer2 also needs EGL & CUDA contexts. But global_context_store
# already has them for cuda:0. Instead of creating new contexts, the store
# will tell rasterizer2 to use them.
rasterizer2 = MeshRasterizerOpenGL(dcs)
rasterize2.rasterize(mesh1)
# When rasterizer1 needs to render on cuda:1, the store will create new contexts.
mesh2 = load_mesh_on_cuda_1()
rasterizer1.rasterize(mesh2)
# When rasterizer1 needs to render on cuda:1, the store will create new contexts.
mesh2 = load_mesh_on_cuda_1()
rasterizer1.rasterize(mesh2)
```
"""
def __init__(self):

1
pytorch3d/renderer/points/pulsar/renderer.py
View File

@ -11,7 +11,6 @@ Proper Python support for pytorch requires creating a torch.autograd.function
here and a torch.nn.Module is exposed for the use in more complex models.
"""
import logging
import math
import warnings
from typing import Optional, Tuple, Union

78
pytorch3d/structures/volumes.py
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@ -97,12 +97,12 @@ class Volumes:
in the world coordinates.
- They are specified with the following mapping that converts
points `x_local` in the local coordinates to points `x_world`
in the world coordinates:
```
x_world = (
x_local * (volume_size - 1) * 0.5 * voxel_size
) - volume_translation,
```
in the world coordinates::
x_world = (
x_local * (volume_size - 1) * 0.5 * voxel_size
) - volume_translation,
here `voxel_size` specifies the size of each voxel of the volume,
and `volume_translation` is the 3D offset of the central voxel of
the volume w.r.t. the origin of the world coordinate frame.
@ -110,12 +110,12 @@ class Volumes:
the world coordinate units. `volume_size` is the spatial size of
the volume in form of a 3D vector `[width, height, depth]`.
- Given the above definition of `x_world`, one can derive the
inverse mapping from `x_world` to `x_local` as follows:
```
x_local = (
(x_world + volume_translation) / (0.5 * voxel_size)
) / (volume_size - 1)
```
inverse mapping from `x_world` to `x_local` as follows::
x_local = (
(x_world + volume_translation) / (0.5 * voxel_size)
) / (volume_size - 1)
- For a trivial volume with `volume_translation==[0, 0, 0]`
with `voxel_size=-1`, `x_world` would range
from -(volume_size-1)/2` to `+(volume_size-1)/2`.
@ -139,13 +139,13 @@ class Volumes:
to `x_local=(-1, 0, 1)`.
- For a "trivial" volume `v` with `voxel_size = 1.`,
`volume_translation=[0., 0., 0.]`, the following holds:
```
torch.nn.functional.grid_sample(
v.densities(),
v.get_coord_grid(world_coordinates=False),
align_corners=True,
) == v.densities(),
```
torch.nn.functional.grid_sample(
v.densities(),
v.get_coord_grid(world_coordinates=False),
align_corners=True,
) == v.densities(),
i.e. sampling the volume at trivial local coordinates
(no scaling with `voxel_size`` or shift with `volume_translation`)
results in the same volume.
@ -588,12 +588,12 @@ class VolumeLocator:
in the world coordinates.
- They are specified with the following mapping that converts
points `x_local` in the local coordinates to points `x_world`
in the world coordinates:
```
x_world = (
x_local * (volume_size - 1) * 0.5 * voxel_size
) - volume_translation,
```
in the world coordinates::
x_world = (
x_local * (volume_size - 1) * 0.5 * voxel_size
) - volume_translation,
here `voxel_size` specifies the size of each voxel of the volume,
and `volume_translation` is the 3D offset of the central voxel of
the volume w.r.t. the origin of the world coordinate frame.
@ -601,12 +601,12 @@ class VolumeLocator:
the world coordinate units. `volume_size` is the spatial size of
the volume in form of a 3D vector `[width, height, depth]`.
- Given the above definition of `x_world`, one can derive the
inverse mapping from `x_world` to `x_local` as follows:
```
x_local = (
(x_world + volume_translation) / (0.5 * voxel_size)
) / (volume_size - 1)
```
inverse mapping from `x_world` to `x_local` as follows::
x_local = (
(x_world + volume_translation) / (0.5 * voxel_size)
) / (volume_size - 1)
- For a trivial volume with `volume_translation==[0, 0, 0]`
with `voxel_size=-1`, `x_world` would range
from -(volume_size-1)/2` to `+(volume_size-1)/2`.
@ -629,14 +629,14 @@ class VolumeLocator:
`DxHxW = 5x5x5`, the point `x_world = (-2, 0, 2)` gets mapped
to `x_local=(-1, 0, 1)`.
- For a "trivial" volume `v` with `voxel_size = 1.`,
`volume_translation=[0., 0., 0.]`, the following holds:
```
torch.nn.functional.grid_sample(
v.densities(),
v.get_coord_grid(world_coordinates=False),
align_corners=True,
) == v.densities(),
```
`volume_translation=[0., 0., 0.]`, the following holds::
torch.nn.functional.grid_sample(
v.densities(),
v.get_coord_grid(world_coordinates=False),
align_corners=True,
) == v.densities(),
i.e. sampling the volume at trivial local coordinates
(no scaling with `voxel_size`` or shift with `volume_translation`)
results in the same volume.

23
pytorch3d/transforms/math.py
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@ -22,18 +22,17 @@ def acos_linear_extrapolation(
domain of `(-1, 1)`. This allows for stable backpropagation in case `x`
is not guaranteed to be strictly within `(-1, 1)`.
More specifically:
```
bounds=(lower_bound, upper_bound)
if lower_bound <= x <= upper_bound:
acos_linear_extrapolation(x) = acos(x)
elif x <= lower_bound: # 1st order Taylor approximation
acos_linear_extrapolation(x)
= acos(lower_bound) + dacos/dx(lower_bound) * (x - lower_bound)
else: # x >= upper_bound
acos_linear_extrapolation(x)
= acos(upper_bound) + dacos/dx(upper_bound) * (x - upper_bound)
```
More specifically::
bounds=(lower_bound, upper_bound)
if lower_bound <= x <= upper_bound:
acos_linear_extrapolation(x) = acos(x)
elif x <= lower_bound: # 1st order Taylor approximation
acos_linear_extrapolation(x)
= acos(lower_bound) + dacos/dx(lower_bound) * (x - lower_bound)
else: # x >= upper_bound
acos_linear_extrapolation(x)
= acos(upper_bound) + dacos/dx(upper_bound) * (x - upper_bound)
Args:
x: Input `Tensor`.

6
pytorch3d/transforms/transform3d.py
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@ -256,12 +256,12 @@ class Transform3d:
The conversion from the 4x4 SE(3) matrix `transform` to the
6D representation `log_transform = [log_translation | log_rotation]`
is done as follows:
```
is done as follows::
log_transform = log(transform.get_matrix())
log_translation = log_transform[3, :3]
log_rotation = inv_hat(log_transform[:3, :3])
```
where `log` is the matrix logarithm
and `inv_hat` is the inverse of the Hat operator [2].

6
pytorch3d/utils/camera_conversions.py
View File

@ -35,10 +35,10 @@ def cameras_from_opencv_projection(
to the NDC screen convention of PyTorch3D.
More specifically, the OpenCV convention projects points to the OpenCV screen
space as follows:
```
space as follows::
x_screen_opencv = camera_matrix @ (R @ x_world + tvec)
```
followed by the homogenization of `x_screen_opencv`.
Note: